Short-term Traffic Volume Prediction for the Merging Roads by Artificial Neural Network

Öz

The forecasting of merging road traffic volume is one of the critical issues for the main networks of traffic-congestion suffering cities. Artificial neural network (ANN) – used in many disciplines varying from economy to different engineering applications such as sales forecasting, industrial process control, customer research, data validation, risk management, target marketing and civil engineering – could be a promising solution to this issue. Providing a higher forecasting accuracy based on past traffic data, ANN has become very popular in transportation engineering for the last 30 years. In this paper, the main goal was to predict the short-term traffic volume of a connection road leading to one of Istanbul’s Bosphorous Bridge in Turkey by the three different implementations of ANN. These were Feed Forward Back Propagation (FFBP), Generalized Regression Neural Network (GRNN) and Radial Based Function (RBF). Then, obtained results were compared with each other and the result of Multi Linear Regression (MLR) method.

Anahtar Kelimeler

• Artificial neural network
• traffic volume
• traffic flow
• short-term prediction

Tali Yollar için Kısa Vadeli Trafik Hacminin Yapay Sinir Ağlarıyla Belirlenmesi

Öz

Ana arterlerinde trafik sıkışıklığı yaşanan şehirlerin ikincil derecedeki yollarında trafik hacim tahminlerinin yapılması kritik konulardan biridir. Ekonomiden farklı mühendislik uygulamalarına kadar birçok alanda (satış tahminleri, endüstriyel süreç kontrolü, müşteri araştırmaları, veri doğrulama, risk yönetimi, hedef pazarlama ve inşaat mühendisliği gibi) kullanılan yapay sinir ağı (YSA) bu konuda umut verici bir çözüm olabilir. Geçmiş trafik verilerine dayanarak daha yüksek bir tahmin doğruluğu sağlayan YSA, son 30 yıldır ulaştırma mühendisliği alanındaki uygulamalarda çok popüler hale geldi. Bu makaledeki temel amaç, İstanbul'un Boğaz Köprülerinden birine katılan bir bağlantı yolunun kısa dönem trafik hacmini YSA'nın üç farklı uygulamasıyla tahmin etmektir. Bunlar İleri Besleme Geri Yayılımı (FFBP), Genelleştirilmiş Regresyon Sinir Ağı (GRNN) ve Radyal Tabanlı Fonksiyon (RBF) idi. Daha sonra elde edilen sonuçlar birbirleriyle ve Çoklu Doğrusal Regresyon (MLR) yönteminin sonuçları ile karşılaştırıldı.

Anahtar Kelimeler

• Yapay sinir ağı
• trafik hacmi
• trafik akışı
• kısa vadeli tahmin

Kaynakça

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